Researchers Identify Fifty New Planets Using Machine Learning

Researchers from the University of Warwick have identified 50 new planets using machine learning algorithms that were used to determine the real, fake or false positives by calculating the probability of each candidate to be a true planet. This is the first time ever that astronomers have used used a process based on machine learning to analyse a sample of potential planets. Previous ML techniques have ranked candidates, but never determined the probability that a candidate was a true planet by themselves. The ML algorithm was built on large samples of thousands of candidates found by telescope missions such as NASA's Kepler and TESS, by researchers from Warwick's Departments of Physics and Computer Science, as well as The Alan Turing Institute. It was trained to recognise real planets using two large samples of confirmed planets and false positives from the now-retired Kepler mission.